干旱区生态环境知识资源中心(Arid): Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China

Arid

DOI	10.1002/hyp.14644
	Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China
	Feng, Ting; Zhu, Shuzhen; Huang, Farong; Hao, Jiansheng; Mind'je, Richard; Zhang, Jiudan; Li, Lanhai
通讯作者	Huang, FR ; Li, LH
来源期刊	HYDROLOGICAL PROCESSES
ISSN	0885-6087
EISSN	1099-1085
出版年	2022
卷号	36 期号:8
英文摘要	Snow density is an essential property of snowpack. To obtain the spatial variability of snow density and estimate it in different periods of the snow season remain challenging, particularly in the mountainous area. This study analysed the spatial variability of snow density with in-situ measurements in three different periods (i.e., accumulation, stable and melt periods) of the snow seasons of 2017/2018 and 2018/2019 in the middle Tianshan Mountains, China. The simulation performances of the multiple linear regression (MLR) model and three machine learning (random forest [RF], extreme gradient boosting [XGB] and light gradient boosting machine [LGBM]) models were evaluated. Results showed that snow density in the melt period (0.27 g cm(-3)) was generally greater than that in the stable (0.20 g cm(-3)) and accumulation periods (0.18 g cm(-3)), and the spatial variability of snow density in the melt period was slightly smaller compared to that in other two periods. The snow density in the mountainous areas was generally higher than that in the plain or oasis areas. It increased significantly (p < 0.05) with elevation during the accumulation and stable periods. In addition to elevation, latitude and ground surface temperature also had critically impacted the spatial variability of snow density in the study area. In the current study, the machine learning models, especially RF, performed better than MLR for simulating snow density in the three periods. Based on the key environmental variables identified by the machine learning model and correlation analysis, this study also provides practical MLR equations to estimate the spatial variance of snow density during different snow periods in the middle Tianshan Mountains. This method can be used for regional snow mass and snow water equivalent prediction, leading to a better understanding of local snow resources.
英文关键词	machine learning middle Tianshan Mountains regression model snow density spatial variability
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000833767300001
WOS关键词	TEMPORAL VARIABILITY ; WATER EQUIVALENT ; COVER ESTIMATION ; RANDOM FOREST ; TIEN-SHAN ; DEPTH
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393054
推荐引用方式 GB/T 7714	Feng, Ting,Zhu, Shuzhen,Huang, Farong,et al. Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China[J],2022,36(8).
APA	Feng, Ting.,Zhu, Shuzhen.,Huang, Farong.,Hao, Jiansheng.,Mind'je, Richard.,...&Li, Lanhai.(2022).Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China.HYDROLOGICAL PROCESSES,36(8).
MLA	Feng, Ting,et al."Spatial variability of snow density and its estimation in different periods of snow season in the middle Tianshan Mountains, China".HYDROLOGICAL PROCESSES 36.8(2022).